Overview

Dataset statistics

Number of variables27
Number of observations113900
Missing cells797300
Missing cells (%)25.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.5 MiB
Average record size in memory216.0 B

Variable types

DateTime1
Text2
Numeric16
Categorical1
Unsupported7

Alerts

new_deceased is highly overall correlated with new_deceased_confirmed_ratioHigh correlation
new_deceased_confirmed_ratio is highly overall correlated with new_deceasedHigh correlation
population is highly overall correlated with population_male and 10 other fieldsHigh correlation
population_male is highly overall correlated with population and 10 other fieldsHigh correlation
population_female is highly overall correlated with population and 10 other fieldsHigh correlation
population_age_00_09 is highly overall correlated with population and 10 other fieldsHigh correlation
population_age_10_19 is highly overall correlated with population and 10 other fieldsHigh correlation
population_age_20_29 is highly overall correlated with population and 10 other fieldsHigh correlation
population_age_30_39 is highly overall correlated with population and 10 other fieldsHigh correlation
population_age_40_49 is highly overall correlated with population and 10 other fieldsHigh correlation
population_age_50_59 is highly overall correlated with population and 10 other fieldsHigh correlation
population_age_60_69 is highly overall correlated with population and 10 other fieldsHigh correlation
population_age_70_79 is highly overall correlated with population and 10 other fieldsHigh correlation
population_age_80_and_older is highly overall correlated with population and 10 other fieldsHigh correlation
life_expectancy has 113900 (100.0%) missing valuesMissing
new_hospitalized_patients has 113900 (100.0%) missing valuesMissing
cumulative_hospitalized_patients has 113900 (100.0%) missing valuesMissing
current_hospitalized_patients has 113900 (100.0%) missing valuesMissing
current_intensive_care_patients has 113900 (100.0%) missing valuesMissing
new_persons_fully_vaccinated has 113900 (100.0%) missing valuesMissing
cumulative_persons_fully_vaccinated has 113900 (100.0%) missing valuesMissing
life_expectancy is an unsupported type, check if it needs cleaning or further analysisUnsupported
new_hospitalized_patients is an unsupported type, check if it needs cleaning or further analysisUnsupported
cumulative_hospitalized_patients is an unsupported type, check if it needs cleaning or further analysisUnsupported
current_hospitalized_patients is an unsupported type, check if it needs cleaning or further analysisUnsupported
current_intensive_care_patients is an unsupported type, check if it needs cleaning or further analysisUnsupported
new_persons_fully_vaccinated is an unsupported type, check if it needs cleaning or further analysisUnsupported
cumulative_persons_fully_vaccinated is an unsupported type, check if it needs cleaning or further analysisUnsupported
new_confirmed has 7499 (6.6%) zerosZeros
new_deceased has 93480 (82.1%) zerosZeros
new_deceased_confirmed_ratio has 85981 (75.5%) zerosZeros
population_male has 3214 (2.8%) zerosZeros
population_female has 3214 (2.8%) zerosZeros
population_density has 3214 (2.8%) zerosZeros
population_age_00_09 has 3214 (2.8%) zerosZeros
population_age_10_19 has 3214 (2.8%) zerosZeros
population_age_20_29 has 3214 (2.8%) zerosZeros
population_age_30_39 has 3214 (2.8%) zerosZeros
population_age_40_49 has 3214 (2.8%) zerosZeros
population_age_50_59 has 3214 (2.8%) zerosZeros
population_age_60_69 has 3214 (2.8%) zerosZeros
population_age_70_79 has 3214 (2.8%) zerosZeros
population_age_80_and_older has 3214 (2.8%) zerosZeros

Reproduction

Analysis started2023-09-07 23:57:25.878659
Analysis finished2023-09-07 23:57:50.728716
Duration24.85 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

date
Date

Distinct330
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size890.0 KiB
Minimum2020-01-02 00:00:00
Maximum2020-12-31 00:00:00
2023-09-08T01:57:50.799064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:50.911002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

week
Text

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size890.0 KiB
2023-09-08T01:57:51.036482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length21
Mean length21
Min length21

Characters and Unicode

Total characters2391900
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row2020-03-09/2020-03-15
2nd row2020-03-16/2020-03-22
3rd row2020-03-16/2020-03-22
4th row2020-03-16/2020-03-22
5th row2020-03-16/2020-03-22
ValueCountFrequency (%)
2020-12-14/2020-12-20 3537
 
3.1%
2020-12-07/2020-12-13 3536
 
3.1%
2020-11-02/2020-11-08 3521
 
3.1%
2020-11-16/2020-11-22 3513
 
3.1%
2020-11-30/2020-12-06 3513
 
3.1%
2020-11-09/2020-11-15 3512
 
3.1%
2020-11-23/2020-11-29 3511
 
3.1%
2020-10-26/2020-11-01 3503
 
3.1%
2020-12-21/2020-12-27 3458
 
3.0%
2020-10-19/2020-10-25 3440
 
3.0%
Other values (42) 78856
69.2%
2023-09-08T01:57:51.268559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 708667
29.6%
2 585461
24.5%
- 455600
19.0%
1 219704
 
9.2%
/ 113900
 
4.8%
3 63685
 
2.7%
9 45601
 
1.9%
6 41489
 
1.7%
4 41388
 
1.7%
8 41134
 
1.7%
Other values (2) 75271
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1822400
76.2%
Dash Punctuation 455600
 
19.0%
Other Punctuation 113900
 
4.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 708667
38.9%
2 585461
32.1%
1 219704
 
12.1%
3 63685
 
3.5%
9 45601
 
2.5%
6 41489
 
2.3%
4 41388
 
2.3%
8 41134
 
2.3%
5 38609
 
2.1%
7 36662
 
2.0%
Dash Punctuation
ValueCountFrequency (%)
- 455600
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 113900
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2391900
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 708667
29.6%
2 585461
24.5%
- 455600
19.0%
1 219704
 
9.2%
/ 113900
 
4.8%
3 63685
 
2.7%
9 45601
 
1.9%
6 41489
 
1.7%
4 41388
 
1.7%
8 41134
 
1.7%
Other values (2) 75271
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2391900
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 708667
29.6%
2 585461
24.5%
- 455600
19.0%
1 219704
 
9.2%
/ 113900
 
4.8%
3 63685
 
2.7%
9 45601
 
1.9%
6 41489
 
1.7%
4 41388
 
1.7%
8 41134
 
1.7%
Other values (2) 75271
 
3.1%
Distinct514
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size890.0 KiB
2023-09-08T01:57:51.443336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.153573
Min length8

Characters and Unicode

Total characters1156492
Distinct characters35
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDE_BB_12051
2nd rowDE_BB_12051
3rd rowDE_BB_12051
4th rowDE_BB_12051
5th rowDE_BB_12051
ValueCountFrequency (%)
it_88_su 312
 
0.3%
it_45_pc 312
 
0.3%
it_78_rc 312
 
0.3%
it_78_vv 312
 
0.3%
it_82_ag 312
 
0.3%
it_82_ct 312
 
0.3%
it_72_sa 312
 
0.3%
it_82_en 312
 
0.3%
it_82_me 312
 
0.3%
it_82_pa 312
 
0.3%
Other values (504) 110780
97.3%
2023-09-08T01:57:51.707859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
_ 227800
19.7%
0 93683
 
8.1%
E 93632
 
8.1%
D 82388
 
7.1%
1 66876
 
5.8%
5 56618
 
4.9%
3 44691
 
3.9%
2 43974
 
3.8%
I 43219
 
3.7%
B 41987
 
3.6%
Other values (25) 361624
31.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 473092
40.9%
Uppercase Letter 455600
39.4%
Connector Punctuation 227800
19.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 93632
20.6%
D 82388
18.1%
I 43219
9.5%
B 41987
9.2%
T 41896
9.2%
N 26502
 
5.8%
W 23392
 
5.1%
Y 18961
 
4.2%
H 13034
 
2.9%
S 12802
 
2.8%
Other values (14) 57787
12.7%
Decimal Number
ValueCountFrequency (%)
0 93683
19.8%
1 66876
14.1%
5 56618
12.0%
3 44691
9.4%
2 43974
9.3%
7 41862
8.8%
6 39215
8.3%
4 32135
 
6.8%
8 27517
 
5.8%
9 26521
 
5.6%
Connector Punctuation
ValueCountFrequency (%)
_ 227800
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 700892
60.6%
Latin 455600
39.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 93632
20.6%
D 82388
18.1%
I 43219
9.5%
B 41987
9.2%
T 41896
9.2%
N 26502
 
5.8%
W 23392
 
5.1%
Y 18961
 
4.2%
H 13034
 
2.9%
S 12802
 
2.8%
Other values (14) 57787
12.7%
Common
ValueCountFrequency (%)
_ 227800
32.5%
0 93683
13.4%
1 66876
 
9.5%
5 56618
 
8.1%
3 44691
 
6.4%
2 43974
 
6.3%
7 41862
 
6.0%
6 39215
 
5.6%
4 32135
 
4.6%
8 27517
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1156492
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 227800
19.7%
0 93683
 
8.1%
E 93632
 
8.1%
D 82388
 
7.1%
1 66876
 
5.8%
5 56618
 
4.9%
3 44691
 
3.9%
2 43974
 
3.8%
I 43219
 
3.7%
B 41987
 
3.6%
Other values (25) 361624
31.3%

new_confirmed
Real number (ℝ)

ZEROS 

Distinct1040
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.065716
Minimum-1165
Maximum4520
Zeros7499
Zeros (%)6.6%
Negative328
Negative (%)0.3%
Memory size890.0 KiB
2023-09-08T01:57:51.823610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1165
5-th percentile0
Q12
median7
Q326
95-th percentile130
Maximum4520
Range5685
Interquartile range (IQR)24

Descriptive statistics

Standard deviation104.22043
Coefficient of variation (CV)3.2502139
Kurtosis377.94201
Mean32.065716
Median Absolute Deviation (MAD)6
Skewness14.952887
Sum3652285
Variance10861.899
MonotonicityNot monotonic
2023-09-08T01:57:51.932819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 16853
 
14.8%
2 10154
 
8.9%
0 7499
 
6.6%
3 7279
 
6.4%
4 5782
 
5.1%
5 4525
 
4.0%
6 3800
 
3.3%
7 3211
 
2.8%
8 2726
 
2.4%
9 2461
 
2.2%
Other values (1030) 49610
43.6%
ValueCountFrequency (%)
-1165 1
< 0.1%
-685 1
< 0.1%
-633 1
< 0.1%
-529 1
< 0.1%
-524 1
< 0.1%
-486 1
< 0.1%
-468 1
< 0.1%
-338 1
< 0.1%
-333 1
< 0.1%
-329 1
< 0.1%
ValueCountFrequency (%)
4520 1
< 0.1%
4451 1
< 0.1%
4296 1
< 0.1%
4066 1
< 0.1%
3979 1
< 0.1%
3730 1
< 0.1%
3706 1
< 0.1%
3695 1
< 0.1%
3654 1
< 0.1%
3613 1
< 0.1%

new_deceased
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct35
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.44817384
Minimum0
Maximum36
Zeros93480
Zeros (%)82.1%
Negative0
Negative (%)0.0%
Memory size890.0 KiB
2023-09-08T01:57:52.035603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum36
Range36
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.5127244
Coefficient of variation (CV)3.3753072
Kurtosis75.907807
Mean0.44817384
Median Absolute Deviation (MAD)0
Skewness7.0054426
Sum51047
Variance2.288335
MonotonicityNot monotonic
2023-09-08T01:57:52.137185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
0 93480
82.1%
1 10448
 
9.2%
2 4107
 
3.6%
3 2019
 
1.8%
4 1150
 
1.0%
5 780
 
0.7%
6 490
 
0.4%
7 388
 
0.3%
8 241
 
0.2%
9 183
 
0.2%
Other values (25) 614
 
0.5%
ValueCountFrequency (%)
0 93480
82.1%
1 10448
 
9.2%
2 4107
 
3.6%
3 2019
 
1.8%
4 1150
 
1.0%
5 780
 
0.7%
6 490
 
0.4%
7 388
 
0.3%
8 241
 
0.2%
9 183
 
0.2%
ValueCountFrequency (%)
36 1
 
< 0.1%
34 2
 
< 0.1%
33 2
 
< 0.1%
32 1
 
< 0.1%
31 2
 
< 0.1%
30 3
< 0.1%
28 3
< 0.1%
27 3
< 0.1%
26 7
< 0.1%
25 4
< 0.1%

new_deceased_confirmed_ratio
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1563
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.020223764
Minimum0
Maximum1
Zeros85981
Zeros (%)75.5%
Negative0
Negative (%)0.0%
Memory size890.0 KiB
2023-09-08T01:57:52.250668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.077582596
Coefficient of variation (CV)3.8362096
Kurtosis90.484659
Mean0.020223764
Median Absolute Deviation (MAD)0
Skewness8.4356582
Sum2303.4867
Variance0.0060190592
MonotonicityNot monotonic
2023-09-08T01:57:52.364836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 85981
75.5%
0.02022376375 7499
 
6.6%
0.25 484
 
0.4%
0.3333333333 471
 
0.4%
0.1428571429 433
 
0.4%
0.1666666667 432
 
0.4%
0.2 424
 
0.4%
0.1111111111 414
 
0.4%
0.5 398
 
0.3%
0.125 386
 
0.3%
Other values (1553) 16978
 
14.9%
ValueCountFrequency (%)
0 85981
75.5%
0.002544529262 1
 
< 0.1%
0.00278551532 1
 
< 0.1%
0.00299850075 1
 
< 0.1%
0.003125 1
 
< 0.1%
0.003367003367 1
 
< 0.1%
0.003703703704 1
 
< 0.1%
0.003731343284 1
 
< 0.1%
0.003773584906 1
 
< 0.1%
0.003992015968 1
 
< 0.1%
ValueCountFrequency (%)
1 374
0.3%
0.8 1
 
< 0.1%
0.75 11
 
< 0.1%
0.7142857143 1
 
< 0.1%
0.6666666667 29
 
< 0.1%
0.6 4
 
< 0.1%
0.5714285714 1
 
< 0.1%
0.5555555556 1
 
< 0.1%
0.55 1
 
< 0.1%
0.5 398
0.3%

country_name
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size890.0 KiB
Germany
81764 
Italy
32136 

Length

Max length7
Median length7
Mean length6.4357155
Min length5

Characters and Unicode

Total characters733028
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGermany
2nd rowGermany
3rd rowGermany
4th rowGermany
5th rowGermany

Common Values

ValueCountFrequency (%)
Germany 81764
71.8%
Italy 32136
 
28.2%

Length

2023-09-08T01:57:52.480008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-08T01:57:52.588346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
germany 81764
71.8%
italy 32136
 
28.2%

Most occurring characters

ValueCountFrequency (%)
a 113900
15.5%
y 113900
15.5%
G 81764
11.2%
e 81764
11.2%
r 81764
11.2%
m 81764
11.2%
n 81764
11.2%
I 32136
 
4.4%
t 32136
 
4.4%
l 32136
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 619128
84.5%
Uppercase Letter 113900
 
15.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 113900
18.4%
y 113900
18.4%
e 81764
13.2%
r 81764
13.2%
m 81764
13.2%
n 81764
13.2%
t 32136
 
5.2%
l 32136
 
5.2%
Uppercase Letter
ValueCountFrequency (%)
G 81764
71.8%
I 32136
 
28.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 733028
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 113900
15.5%
y 113900
15.5%
G 81764
11.2%
e 81764
11.2%
r 81764
11.2%
m 81764
11.2%
n 81764
11.2%
I 32136
 
4.4%
t 32136
 
4.4%
l 32136
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 733028
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 113900
15.5%
y 113900
15.5%
G 81764
11.2%
e 81764
11.2%
r 81764
11.2%
m 81764
11.2%
n 81764
11.2%
I 32136
 
4.4%
t 32136
 
4.4%
l 32136
 
4.4%

population
Real number (ℝ)

HIGH CORRELATION 

Distinct513
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean316364.01
Minimum34209
Maximum4342212
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size890.0 KiB
2023-09-08T01:57:52.697095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34209
5-th percentile72124
Q1133210
median219320
Q3348871
95-th percentile890768
Maximum4342212
Range4308003
Interquartile range (IQR)215661

Descriptive statistics

Standard deviation374744.92
Coefficient of variation (CV)1.1845372
Kurtosis47.608698
Mean316364.01
Median Absolute Deviation (MAD)97044.006
Skewness5.7062109
Sum3.603386 × 1010
Variance1.4043376 × 1011
MonotonicityNot monotonic
2023-09-08T01:57:52.819545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
316364.0064 624
 
0.5%
330211 312
 
0.3%
705393 312
 
0.3%
139403 312
 
0.3%
312533 312
 
0.3%
234493 312
 
0.3%
528791 312
 
0.3%
841180 312
 
0.3%
213840 312
 
0.3%
219556 312
 
0.3%
Other values (503) 110468
97.0%
ValueCountFrequency (%)
34209 105
0.1%
34835 101
0.1%
40403 100
0.1%
40792 151
0.1%
41249 165
0.1%
41847 159
0.1%
41970 147
0.1%
42520 190
0.2%
43837 151
0.1%
43893 162
0.1%
ValueCountFrequency (%)
4342212 312
0.3%
3250315 312
0.3%
2259523 312
0.3%
1841179 302
0.3%
1471508 305
0.3%
1265954 312
0.3%
1252588 312
0.3%
1251994 312
0.3%
1157624 296
0.3%
1114590 312
0.3%

population_male
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct500
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean151216.88
Minimum0
Maximum2081239
Zeros3214
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size890.0 KiB
2023-09-08T01:57:52.932098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile25344
Q163859
median104015
Q3170965
95-th percentile436607
Maximum2081239
Range2081239
Interquartile range (IQR)107106

Descriptive statistics

Standard deviation182962.34
Coefficient of variation (CV)1.2099333
Kurtosis44.83821
Mean151216.88
Median Absolute Deviation (MAD)48250
Skewness5.5084659
Sum1.7223603 × 1010
Variance3.3475217 × 1010
MonotonicityNot monotonic
2023-09-08T01:57:53.158319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3214
 
2.8%
151216.8808 624
 
0.5%
161886 312
 
0.3%
191995 312
 
0.3%
454113 312
 
0.3%
68522 312
 
0.3%
153466 312
 
0.3%
112538 312
 
0.3%
256276 312
 
0.3%
400324 312
 
0.3%
Other values (490) 107566
94.4%
ValueCountFrequency (%)
0 3214
2.8%
16954 105
 
0.1%
16960 101
 
0.1%
19780 100
 
0.1%
19895 151
 
0.1%
20048 159
 
0.1%
20162 165
 
0.1%
20542 190
 
0.2%
20673 147
 
0.1%
21610 162
 
0.1%
ValueCountFrequency (%)
2081239 312
0.3%
1576316 312
0.3%
1092504 312
0.3%
902048 302
0.3%
717308 305
0.3%
624201 312
0.3%
610856 312
0.3%
605997 312
0.3%
567201 296
0.3%
552870 312
0.3%

population_female
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct500
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean157346.42
Minimum0
Maximum2260973
Zeros3214
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size890.0 KiB
2023-09-08T01:57:53.269713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27125
Q165016
median107217
Q3175701
95-th percentile457038
Maximum2260973
Range2260973
Interquartile range (IQR)110685

Descriptive statistics

Standard deviation195120.97
Coefficient of variation (CV)1.2400725
Kurtosis47.757772
Mean157346.42
Median Absolute Deviation (MAD)50129.424
Skewness5.6943333
Sum1.7921758 × 1010
Variance3.8072193 × 1010
MonotonicityNot monotonic
2023-09-08T01:57:53.382039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3214
 
2.8%
157346.4239 624
 
0.5%
58396 359
 
0.3%
168325 312
 
0.3%
359999 312
 
0.3%
159067 312
 
0.3%
121955 312
 
0.3%
272515 312
 
0.3%
440856 312
 
0.3%
110765 312
 
0.3%
Other values (490) 107519
94.4%
ValueCountFrequency (%)
0 3214
2.8%
17255 105
 
0.1%
17875 101
 
0.1%
20623 100
 
0.1%
20897 151
 
0.1%
21087 165
 
0.1%
21297 147
 
0.1%
21799 159
 
0.1%
21978 190
 
0.2%
22127 151
 
0.1%
ValueCountFrequency (%)
2260973 312
0.3%
1673999 312
0.3%
1167019 312
0.3%
939131 302
0.3%
754200 305
0.3%
646591 312
0.3%
641753 312
0.3%
641138 312
0.3%
590423 296
0.3%
569300 312
0.3%

population_density
Real number (ℝ)

ZEROS 

Distinct483
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean492.25816
Minimum0
Maximum4721.9
Zeros3214
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size890.0 KiB
2023-09-08T01:57:53.491467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile55.9
Q1117.6
median203.5
Q3495.9
95-th percentile2107.1
Maximum4721.9
Range4721.9
Interquartile range (IQR)378.3

Descriptive statistics

Standard deviation677.7734
Coefficient of variation (CV)1.3768657
Kurtosis6.853509
Mean492.25816
Median Absolute Deviation (MAD)110.6
Skewness2.4777125
Sum56068205
Variance459376.78
MonotonicityNot monotonic
2023-09-08T01:57:53.600741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3214
 
2.8%
117.6 641
 
0.6%
55.9 624
 
0.5%
492.2581615 624
 
0.5%
222.3 624
 
0.5%
57.4 624
 
0.5%
140 530
 
0.5%
181.7 526
 
0.5%
102 519
 
0.5%
157.8 514
 
0.5%
Other values (473) 105460
92.6%
ValueCountFrequency (%)
0 3214
2.8%
36.2 100
 
0.1%
36.8 115
 
0.1%
39.3 312
 
0.3%
39.8 92
 
0.1%
40.1 133
 
0.1%
40.2 107
 
0.1%
45.6 153
 
0.1%
47.1 128
 
0.1%
50 312
 
0.3%
ValueCountFrequency (%)
4721.9 305
0.3%
3076.6 284
0.2%
3067.3 225
0.2%
3061.1 295
0.3%
3001.4 291
0.3%
2902.3 249
0.2%
2817.6 240
0.2%
2793.4 239
0.2%
2791.3 301
0.3%
2741.9 242
0.2%

population_age_00_09
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct497
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27062.015
Minimum0
Maximum379592
Zeros3214
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size890.0 KiB
2023-09-08T01:57:53.709172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4314
Q111178
median18239
Q329695
95-th percentile78697
Maximum379592
Range379592
Interquartile range (IQR)18517

Descriptive statistics

Standard deviation33405.449
Coefficient of variation (CV)1.2344036
Kurtosis44.554567
Mean27062.015
Median Absolute Deviation (MAD)8393
Skewness5.5052307
Sum3.0823635 × 109
Variance1.115924 × 109
MonotonicityNot monotonic
2023-09-08T01:57:53.823479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3214
 
2.8%
27062.01513 624
 
0.5%
15715 624
 
0.5%
3822 367
 
0.3%
13483 358
 
0.3%
23369 312
 
0.3%
84871 312
 
0.3%
83479 312
 
0.3%
10479 312
 
0.3%
26921 312
 
0.3%
Other values (487) 107153
94.1%
ValueCountFrequency (%)
0 3214
2.8%
2458 101
 
0.1%
2895 105
 
0.1%
3223 165
 
0.1%
3413 147
 
0.1%
3433 190
 
0.2%
3442 100
 
0.1%
3753 92
 
0.1%
3822 367
 
0.3%
3832 151
 
0.1%
ValueCountFrequency (%)
379592 312
0.3%
284923 312
0.3%
182491 312
0.3%
181894 302
0.3%
142225 305
0.3%
115453 312
0.3%
115371 312
0.3%
107501 296
0.3%
105036 312
0.3%
104525 312
0.3%

population_age_10_19
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct500
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28982.423
Minimum0
Maximum408705
Zeros3214
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size890.0 KiB
2023-09-08T01:57:53.940827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4608
Q112236
median19061
Q333130
95-th percentile88403
Maximum408705
Range408705
Interquartile range (IQR)20894

Descriptive statistics

Standard deviation35530.467
Coefficient of variation (CV)1.2259316
Kurtosis45.696709
Mean28982.423
Median Absolute Deviation (MAD)9146
Skewness5.5279572
Sum3.301098 × 109
Variance1.2624141 × 109
MonotonicityNot monotonic
2023-09-08T01:57:54.068416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3214
 
2.8%
28982.42276 624
 
0.5%
28188 312
 
0.3%
36250 312
 
0.3%
90248 312
 
0.3%
12089 312
 
0.3%
29388 312
 
0.3%
18738 312
 
0.3%
45807 312
 
0.3%
69832 312
 
0.3%
Other values (490) 107566
94.4%
ValueCountFrequency (%)
0 3214
2.8%
2373 101
 
0.1%
3149 105
 
0.1%
3461 165
 
0.1%
3524 100
 
0.1%
3728 147
 
0.1%
3783 190
 
0.2%
3930 310
 
0.3%
4034 164
 
0.1%
4065 194
 
0.2%
ValueCountFrequency (%)
408705 312
0.3%
302663 312
0.3%
202287 312
0.3%
159102 302
0.3%
131684 312
0.3%
127657 312
0.3%
125002 312
0.3%
117281 312
0.3%
115475 312
0.3%
115310 305
0.3%

population_age_20_29
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct499
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34212.49
Minimum0
Maximum421150
Zeros3214
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size890.0 KiB
2023-09-08T01:57:54.190283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5611
Q113912
median22074
Q339546
95-th percentile92088
Maximum421150
Range421150
Interquartile range (IQR)25634

Descriptive statistics

Standard deviation40865.562
Coefficient of variation (CV)1.1944632
Kurtosis31.255266
Mean34212.49
Median Absolute Deviation (MAD)11076
Skewness4.6358407
Sum3.8968026 × 109
Variance1.6699942 × 109
MonotonicityNot monotonic
2023-09-08T01:57:54.301097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3214
 
2.8%
34212.49033 624
 
0.5%
16054 624
 
0.5%
7300 372
 
0.3%
32893 312
 
0.3%
68749 312
 
0.3%
29345 312
 
0.3%
21590 312
 
0.3%
46952 312
 
0.3%
75248 312
 
0.3%
Other values (489) 107194
94.1%
ValueCountFrequency (%)
0 3214
2.8%
2730 101
 
0.1%
4109 172
 
0.2%
4138 92
 
0.1%
4187 105
 
0.1%
4461 151
 
0.1%
4665 100
 
0.1%
4764 130
 
0.1%
4805 129
 
0.1%
5113 117
 
0.1%
ValueCountFrequency (%)
421150 312
0.3%
315630 312
0.3%
260498 302
0.3%
226692 305
0.3%
210921 312
0.3%
166203 301
0.3%
148893 296
0.3%
146846 312
0.3%
140387 312
0.3%
132589 312
0.3%

population_age_30_39
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct500
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37800.368
Minimum0
Maximum528328
Zeros3214
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size890.0 KiB
2023-09-08T01:57:54.417562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6277
Q115311
median24840
Q341554
95-th percentile107958
Maximum528328
Range528328
Interquartile range (IQR)26243

Descriptive statistics

Standard deviation48043.881
Coefficient of variation (CV)1.2709898
Kurtosis41.164908
Mean37800.368
Median Absolute Deviation (MAD)11669
Skewness5.3731955
Sum4.3054619 × 109
Variance2.3082145 × 109
MonotonicityNot monotonic
2023-09-08T01:57:54.555255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3214
 
2.8%
37800.36815 624
 
0.5%
35701 548
 
0.5%
38125 312
 
0.3%
81437 312
 
0.3%
13942 312
 
0.3%
23750 312
 
0.3%
55952 312
 
0.3%
81444 312
 
0.3%
21454 312
 
0.3%
Other values (490) 107330
94.2%
ValueCountFrequency (%)
0 3214
2.8%
3548 101
 
0.1%
4277 105
 
0.1%
4282 100
 
0.1%
4723 92
 
0.1%
4912 151
 
0.1%
5117 190
 
0.2%
5189 147
 
0.1%
5248 159
 
0.1%
5262 165
 
0.1%
ValueCountFrequency (%)
528328 312
0.3%
402505 312
0.3%
295219 302
0.3%
257247 305
0.3%
249499 312
0.3%
172819 301
0.3%
154524 312
0.3%
151407 296
0.3%
151258 312
0.3%
150204 312
0.3%

population_age_40_49
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct498
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43123.205
Minimum0
Maximum712014
Zeros3214
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size890.0 KiB
2023-09-08T01:57:54.689381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6159
Q116352
median26507
Q349024
95-th percentile140011
Maximum712014
Range712014
Interquartile range (IQR)32672

Descriptive statistics

Standard deviation59441.413
Coefficient of variation (CV)1.378409
Kurtosis55.435225
Mean43123.205
Median Absolute Deviation (MAD)13326
Skewness6.1862618
Sum4.9117331 × 109
Variance3.5332815 × 109
MonotonicityNot monotonic
2023-09-08T01:57:54.839235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3214
 
2.8%
43123.20543 624
 
0.5%
24422 463
 
0.4%
17242 351
 
0.3%
52253 312
 
0.3%
160939 312
 
0.3%
145766 312
 
0.3%
21547 312
 
0.3%
48944 312
 
0.3%
34367 312
 
0.3%
Other values (488) 107376
94.3%
ValueCountFrequency (%)
0 3214
2.8%
3715 101
 
0.1%
3952 105
 
0.1%
4481 100
 
0.1%
4661 165
 
0.1%
4991 147
 
0.1%
5092 159
 
0.1%
5100 190
 
0.2%
5204 162
 
0.1%
5280 151
 
0.1%
ValueCountFrequency (%)
712014 312
0.3%
519672 312
0.3%
346259 312
0.3%
248287 302
0.3%
201449 305
0.3%
200736 312
0.3%
188992 312
0.3%
181778 312
0.3%
174903 312
0.3%
162058 312
0.3%

population_age_50_59
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct499
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48923.286
Minimum0
Maximum695965
Zeros3214
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size890.0 KiB
2023-09-08T01:57:54.983581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7701
Q121465
median34465
Q355592
95-th percentile141078
Maximum695965
Range695965
Interquartile range (IQR)34127

Descriptive statistics

Standard deviation58310.771
Coefficient of variation (CV)1.1918817
Kurtosis51.649821
Mean48923.286
Median Absolute Deviation (MAD)15503
Skewness5.8892381
Sum5.5723623 × 109
Variance3.400146 × 109
MonotonicityNot monotonic
2023-09-08T01:57:55.140017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3214
 
2.8%
48923.28631 624
 
0.5%
51041 469
 
0.4%
21227 401
 
0.4%
53864 312
 
0.3%
56389 312
 
0.3%
22812 312
 
0.3%
48354 312
 
0.3%
37143 312
 
0.3%
85879 312
 
0.3%
Other values (489) 107320
94.2%
ValueCountFrequency (%)
0 3214
2.8%
5393 105
 
0.1%
5829 101
 
0.1%
6502 165
 
0.1%
6569 159
 
0.1%
6663 100
 
0.1%
6720 147
 
0.1%
6857 151
 
0.1%
6867 190
 
0.2%
6925 151
 
0.1%
ValueCountFrequency (%)
695965 312
0.3%
499331 312
0.3%
348921 312
0.3%
265199 302
0.3%
198358 305
0.3%
195382 312
0.3%
191931 312
0.3%
183776 312
0.3%
181023 296
0.3%
174833 312
0.3%

population_age_60_69
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct496
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37709.616
Minimum0
Maximum501458
Zeros3214
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size890.0 KiB
2023-09-08T01:57:55.304121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6638
Q116281
median27203
Q343556
95-th percentile106779
Maximum501458
Range501458
Interquartile range (IQR)27275

Descriptive statistics

Standard deviation43295.89
Coefficient of variation (CV)1.1481393
Kurtosis46.19319
Mean37709.616
Median Absolute Deviation (MAD)12295
Skewness5.5448584
Sum4.2951253 × 109
Variance1.8745341 × 109
MonotonicityNot monotonic
2023-09-08T01:57:55.464030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3214
 
2.8%
37709.61593 624
 
0.5%
30380 542
 
0.5%
13975 357
 
0.3%
12837 343
 
0.3%
14258 329
 
0.3%
120212 312
 
0.3%
106779 312
 
0.3%
17322 312
 
0.3%
38061 312
 
0.3%
Other values (486) 107243
94.2%
ValueCountFrequency (%)
0 3214
2.8%
4668 105
 
0.1%
4727 151
 
0.1%
5035 159
 
0.1%
5202 165
 
0.1%
5212 151
 
0.1%
5338 125
 
0.1%
5349 190
 
0.2%
5426 162
 
0.1%
5557 147
 
0.1%
ValueCountFrequency (%)
501458 312
0.3%
364488 312
0.3%
285323 312
0.3%
176421 302
0.3%
153102 312
0.3%
151058 312
0.3%
147003 312
0.3%
133674 305
0.3%
132882 312
0.3%
132280 296
0.3%

population_age_70_79
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct498
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29510.069
Minimum0
Maximum406123
Zeros3214
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size890.0 KiB
2023-09-08T01:57:55.739028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5086
Q111634
median20305
Q333907
95-th percentile88923
Maximum406123
Range406123
Interquartile range (IQR)22273

Descriptive statistics

Standard deviation36326.67
Coefficient of variation (CV)1.2309924
Kurtosis45.649588
Mean29510.069
Median Absolute Deviation (MAD)9689
Skewness5.6215646
Sum3.3611968 × 109
Variance1.319627 × 109
MonotonicityNot monotonic
2023-09-08T01:57:55.897404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3214
 
2.8%
29510.06866 624
 
0.5%
26186 475
 
0.4%
12867 342
 
0.3%
34608 312
 
0.3%
41359 312
 
0.3%
32881 312
 
0.3%
29512 312
 
0.3%
62731 312
 
0.3%
102845 312
 
0.3%
Other values (488) 107373
94.3%
ValueCountFrequency (%)
0 3214
2.8%
3195 105
 
0.1%
3733 125
 
0.1%
3818 159
 
0.1%
4064 147
 
0.1%
4069 100
 
0.1%
4106 165
 
0.1%
4134 151
 
0.1%
4190 151
 
0.1%
4246 190
 
0.2%
ValueCountFrequency (%)
406123 312
0.3%
324842 312
0.3%
250534 312
0.3%
151580 302
0.3%
121655 305
0.3%
120133 312
0.3%
117118 312
0.3%
112213 312
0.3%
109916 312
0.3%
106547 296
0.3%

population_age_80_and_older
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct498
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21239.832
Minimum0
Maximum288877
Zeros3214
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size890.0 KiB
2023-09-08T01:57:56.053941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3605
Q18123
median14492
Q324555
95-th percentile63498
Maximum288877
Range288877
Interquartile range (IQR)16432

Descriptive statistics

Standard deviation26074.52
Coefficient of variation (CV)1.2276237
Kurtosis45.332847
Mean21239.832
Median Absolute Deviation (MAD)7048
Skewness5.6029078
Sum2.4192169 × 109
Variance6.7988062 × 108
MonotonicityNot monotonic
2023-09-08T01:57:56.207500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3214
 
2.8%
21239.832 624
 
0.5%
11155 398
 
0.3%
22701 312
 
0.3%
52560 312
 
0.3%
22938 312
 
0.3%
22596 312
 
0.3%
42503 312
 
0.3%
84775 312
 
0.3%
20416 312
 
0.3%
Other values (488) 107480
94.4%
ValueCountFrequency (%)
0 3214
2.8%
2493 105
 
0.1%
2591 151
 
0.1%
2767 125
 
0.1%
2780 159
 
0.1%
2894 151
 
0.1%
2969 194
 
0.2%
2991 101
 
0.1%
3077 190
 
0.2%
3107 101
 
0.1%
ValueCountFrequency (%)
288877 312
0.3%
236261 312
0.3%
183288 312
0.3%
102979 302
0.3%
87111 312
0.3%
85438 312
0.3%
84775 312
0.3%
81971 312
0.3%
79168 312
0.3%
76176 296
0.3%

life_expectancy
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing113900
Missing (%)100.0%
Memory size890.0 KiB

new_hospitalized_patients
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing113900
Missing (%)100.0%
Memory size890.0 KiB

cumulative_hospitalized_patients
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing113900
Missing (%)100.0%
Memory size890.0 KiB

current_hospitalized_patients
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing113900
Missing (%)100.0%
Memory size890.0 KiB

current_intensive_care_patients
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing113900
Missing (%)100.0%
Memory size890.0 KiB

new_persons_fully_vaccinated
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing113900
Missing (%)100.0%
Memory size890.0 KiB

cumulative_persons_fully_vaccinated
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing113900
Missing (%)100.0%
Memory size890.0 KiB

Interactions

2023-09-08T01:57:48.232628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:28.116679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:29.396370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:30.627551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:31.825388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:33.061783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:34.233765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:35.419467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:36.696584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:37.911500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:39.236417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:40.618857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:42.057414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:43.599906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:45.241304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:46.723629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:48.324518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:28.200520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:29.470659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:30.706059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:31.895265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:33.132401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:34.305459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:35.494060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:36.769443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:37.986061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:39.314528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:40.695531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:42.149072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:43.688783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:45.329121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:46.820483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:48.421673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:28.277822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:29.549758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:30.779340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:31.969043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:33.205365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:34.380224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:35.565680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:36.845645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:38.070654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:39.394722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:40.783696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:42.246071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:43.782358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:45.420250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:46.916150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:48.516081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:28.349901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:29.623384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:30.852673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:32.037640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:33.275178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:34.450325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:35.632287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:36.919410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:38.147345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:39.480958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:40.868715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:42.342335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:43.871325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:45.508911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:47.012391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:48.606447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:28.423540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:29.695607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:30.923254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:32.105810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:33.342178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:34.522937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:35.702804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:36.995211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:38.223134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:39.555938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:40.949279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:42.431378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:43.957651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:45.591249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:47.097692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:48.694654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:28.499190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:29.771846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:30.995701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:32.267523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:33.409511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:34.592194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:35.771988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:37.067554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:38.302640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:39.630299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:41.035122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:42.530365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:44.063569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:45.680222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:47.190166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:48.783837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:28.574781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:29.848497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:31.063305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:32.335718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:33.482311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:34.663317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:35.840321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:37.143267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:38.386453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:39.705043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:41.123283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:42.621127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:44.162705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:45.766177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:47.275430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:48.871916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:28.644334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:29.920989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:31.134719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:32.402254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:33.551123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:34.733818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:35.908383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:37.213197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:38.467124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:39.775388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:41.207722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:42.712496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:44.261209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:45.848659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:47.365605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:48.966147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:28.793407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:29.999969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:31.211745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:32.476539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:33.625126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:34.812104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:35.986349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:37.287827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:38.555130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:39.854356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:41.308697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:42.814682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:44.365880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:45.944566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:47.463928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:49.183825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:28.874065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:30.077571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:31.289259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:32.555757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:33.705577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:34.895092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:36.162432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:37.369199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:38.641272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:40.053223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:41.404613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:42.920551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:44.576322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:46.042100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:47.565523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:49.276244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:28.944385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:30.147751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:31.363429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:32.623541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:33.772834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:34.965096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:36.229192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:37.442768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:38.714749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:40.128060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:41.497467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:43.020981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:44.660359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:46.125784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:47.655361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:49.372589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:29.017786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:30.226364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:31.433982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:32.692797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:33.844805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:35.040261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:36.298741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:37.522361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:38.797932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:40.204370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:41.587365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:43.115860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:44.747196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:46.225484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:47.746440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:49.468665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:29.098897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:30.307685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:31.517100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:32.767667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:33.921671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:35.118289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:36.370635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:37.605579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:38.882360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:40.287411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:41.681355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:43.214497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:44.840188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:46.332736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:47.844394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:49.562095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:29.172543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:30.384191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:31.597736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:32.842600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:34.004908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:35.195939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:36.445605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:37.684863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:38.974503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:40.374017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:41.774651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:43.314092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:44.949515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:46.432522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:47.946301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:49.649443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:29.243886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:30.462006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:31.667723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:32.908575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:34.077200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:35.268068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:36.529222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:37.759508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:39.058260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:40.456264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:41.861252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:43.404758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:45.045736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:46.523121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:48.039428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:49.741728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:29.321089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:30.547362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:31.744179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:32.989931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:34.157574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:35.344784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:36.620799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:37.837904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:39.153703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:40.540433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:41.959392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:43.505481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:45.149326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:46.633925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-08T01:57:48.139202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-09-08T01:57:56.333695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
new_confirmednew_deceasednew_deceased_confirmed_ratiopopulationpopulation_malepopulation_femalepopulation_densitypopulation_age_00_09population_age_10_19population_age_20_29population_age_30_39population_age_40_49population_age_50_59population_age_60_69population_age_70_79population_age_80_and_oldercountry_name
new_confirmed1.0000.4150.1340.1940.1750.1730.1360.1880.1750.1740.1780.1640.1760.1670.1620.1570.122
new_deceased0.4151.0000.858-0.041-0.051-0.0550.075-0.031-0.049-0.044-0.040-0.075-0.047-0.051-0.066-0.0740.117
new_deceased_confirmed_ratio0.1340.8581.0000.0030.001-0.000-0.0070.0010.001-0.0000.004-0.004-0.0000.004-0.002-0.0020.141
population0.194-0.0410.0031.0000.9340.9340.2580.9240.9270.8890.9240.9280.9280.9200.9180.9120.388
population_male0.175-0.0510.0010.9341.0000.9990.3440.9920.9930.9570.9900.9940.9940.9860.9830.9770.399
population_female0.173-0.055-0.0000.9340.9991.0000.3480.9890.9920.9550.9890.9940.9940.9870.9860.9810.389
population_density0.1360.075-0.0070.2580.3440.3481.0000.3880.3430.4670.3990.3150.3070.2700.2950.2830.265
population_age_00_090.188-0.0310.0010.9240.9920.9890.3881.0000.9940.9600.9900.9810.9840.9680.9600.9500.347
population_age_10_190.175-0.0490.0010.9270.9930.9920.3430.9941.0000.9580.9830.9880.9890.9730.9660.9580.372
population_age_20_290.174-0.044-0.0000.8890.9570.9550.4670.9600.9581.0000.9720.9450.9290.9070.9070.9020.335
population_age_30_390.178-0.0400.0040.9240.9900.9890.3990.9900.9830.9721.0000.9800.9750.9640.9590.9520.370
population_age_40_490.164-0.075-0.0040.9280.9940.9940.3150.9810.9880.9450.9801.0000.9880.9790.9820.9770.443
population_age_50_590.176-0.047-0.0000.9280.9940.9940.3070.9840.9890.9290.9750.9881.0000.9920.9840.9770.350
population_age_60_690.167-0.0510.0040.9200.9860.9870.2700.9680.9730.9070.9640.9790.9921.0000.9890.9860.396
population_age_70_790.162-0.066-0.0020.9180.9830.9860.2950.9600.9660.9070.9590.9820.9840.9891.0000.9940.464
population_age_80_and_older0.157-0.074-0.0020.9120.9770.9810.2830.9500.9580.9020.9520.9770.9770.9860.9941.0000.488
country_name0.1220.1170.1410.3880.3990.3890.2650.3470.3720.3350.3700.4430.3500.3960.4640.4881.000

Missing values

2023-09-08T01:57:49.896316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-08T01:57:50.315263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

dateweeklocation_keynew_confirmednew_deceasednew_deceased_confirmed_ratiocountry_namepopulationpopulation_malepopulation_femalepopulation_densitypopulation_age_00_09population_age_10_19population_age_20_29population_age_30_39population_age_40_49population_age_50_59population_age_60_69population_age_70_79population_age_80_and_olderlife_expectancynew_hospitalized_patientscumulative_hospitalized_patientscurrent_hospitalized_patientscurrent_intensive_care_patientsnew_persons_fully_vaccinatedcumulative_persons_fully_vaccinated
02020-03-152020-03-09/2020-03-15DE_BB_120512.00.00.0Germany72124.035617.036507.0367.46029.05183.06646.09776.07690.011604.010152.08681.06363.0NaNNaNNaNNaNNaNNaNNaN
12020-03-172020-03-16/2020-03-22DE_BB_120511.00.00.0Germany72124.035617.036507.0367.46029.05183.06646.09776.07690.011604.010152.08681.06363.0NaNNaNNaNNaNNaNNaNNaN
22020-03-192020-03-16/2020-03-22DE_BB_120512.00.00.0Germany72124.035617.036507.0367.46029.05183.06646.09776.07690.011604.010152.08681.06363.0NaNNaNNaNNaNNaNNaNNaN
32020-03-202020-03-16/2020-03-22DE_BB_120511.00.00.0Germany72124.035617.036507.0367.46029.05183.06646.09776.07690.011604.010152.08681.06363.0NaNNaNNaNNaNNaNNaNNaN
42020-03-222020-03-16/2020-03-22DE_BB_120512.00.00.0Germany72124.035617.036507.0367.46029.05183.06646.09776.07690.011604.010152.08681.06363.0NaNNaNNaNNaNNaNNaNNaN
52020-03-232020-03-23/2020-03-29DE_BB_120513.00.00.0Germany72124.035617.036507.0367.46029.05183.06646.09776.07690.011604.010152.08681.06363.0NaNNaNNaNNaNNaNNaNNaN
62020-03-242020-03-23/2020-03-29DE_BB_120511.00.00.0Germany72124.035617.036507.0367.46029.05183.06646.09776.07690.011604.010152.08681.06363.0NaNNaNNaNNaNNaNNaNNaN
72020-03-262020-03-23/2020-03-29DE_BB_120512.00.00.0Germany72124.035617.036507.0367.46029.05183.06646.09776.07690.011604.010152.08681.06363.0NaNNaNNaNNaNNaNNaNNaN
82020-03-282020-03-23/2020-03-29DE_BB_120511.00.00.0Germany72124.035617.036507.0367.46029.05183.06646.09776.07690.011604.010152.08681.06363.0NaNNaNNaNNaNNaNNaNNaN
92020-03-292020-03-23/2020-03-29DE_BB_120511.00.00.0Germany72124.035617.036507.0367.46029.05183.06646.09776.07690.011604.010152.08681.06363.0NaNNaNNaNNaNNaNNaNNaN
dateweeklocation_keynew_confirmednew_deceasednew_deceased_confirmed_ratiocountry_namepopulationpopulation_malepopulation_femalepopulation_densitypopulation_age_00_09population_age_10_19population_age_20_29population_age_30_39population_age_40_49population_age_50_59population_age_60_69population_age_70_79population_age_80_and_olderlife_expectancynew_hospitalized_patientscumulative_hospitalized_patientscurrent_hospitalized_patientscurrent_intensive_care_patientsnew_persons_fully_vaccinatedcumulative_persons_fully_vaccinated
1138902020-12-222020-12-21/2020-12-27IT_88_SU33.00.00.0Italy316364.006436151216.880822157346.423885492.25816127062.01513128982.42276434212.49033337800.36815443123.20542748923.2863137709.61592929510.06865521239.832003NaNNaNNaNNaNNaNNaNNaN
1138912020-12-232020-12-21/2020-12-27IT_88_SU67.00.00.0Italy316364.006436151216.880822157346.423885492.25816127062.01513128982.42276434212.49033337800.36815443123.20542748923.2863137709.61592929510.06865521239.832003NaNNaNNaNNaNNaNNaNNaN
1138922020-12-242020-12-21/2020-12-27IT_88_SU75.00.00.0Italy316364.006436151216.880822157346.423885492.25816127062.01513128982.42276434212.49033337800.36815443123.20542748923.2863137709.61592929510.06865521239.832003NaNNaNNaNNaNNaNNaNNaN
1138932020-12-252020-12-21/2020-12-27IT_88_SU81.00.00.0Italy316364.006436151216.880822157346.423885492.25816127062.01513128982.42276434212.49033337800.36815443123.20542748923.2863137709.61592929510.06865521239.832003NaNNaNNaNNaNNaNNaNNaN
1138942020-12-262020-12-21/2020-12-27IT_88_SU41.00.00.0Italy316364.006436151216.880822157346.423885492.25816127062.01513128982.42276434212.49033337800.36815443123.20542748923.2863137709.61592929510.06865521239.832003NaNNaNNaNNaNNaNNaNNaN
1138952020-12-272020-12-21/2020-12-27IT_88_SU10.00.00.0Italy316364.006436151216.880822157346.423885492.25816127062.01513128982.42276434212.49033337800.36815443123.20542748923.2863137709.61592929510.06865521239.832003NaNNaNNaNNaNNaNNaNNaN
1138962020-12-282020-12-28/2021-01-03IT_88_SU44.00.00.0Italy316364.006436151216.880822157346.423885492.25816127062.01513128982.42276434212.49033337800.36815443123.20542748923.2863137709.61592929510.06865521239.832003NaNNaNNaNNaNNaNNaNNaN
1138972020-12-292020-12-28/2021-01-03IT_88_SU22.00.00.0Italy316364.006436151216.880822157346.423885492.25816127062.01513128982.42276434212.49033337800.36815443123.20542748923.2863137709.61592929510.06865521239.832003NaNNaNNaNNaNNaNNaNNaN
1138982020-12-302020-12-28/2021-01-03IT_88_SU78.00.00.0Italy316364.006436151216.880822157346.423885492.25816127062.01513128982.42276434212.49033337800.36815443123.20542748923.2863137709.61592929510.06865521239.832003NaNNaNNaNNaNNaNNaNNaN
1138992020-12-312020-12-28/2021-01-03IT_88_SU84.00.00.0Italy316364.006436151216.880822157346.423885492.25816127062.01513128982.42276434212.49033337800.36815443123.20542748923.2863137709.61592929510.06865521239.832003NaNNaNNaNNaNNaNNaNNaN